class_docstr: ProcessGroup
  Process Group indicates how processes are organized in groups for parallel execution using Tensor Parallelism and Data Parallelism.
  
  NOTE, the ProcessGroup must be used after `torch.distributed.initialize()`
  
  
  Args:
      rank: the global rank of the current process.
      ranks: List[int], a list of rank id belongings to this process group.
      backend: str, the backend of the process group.
      tp_degree: Optional[int], tensor parallelism degree. How many processes are inside a tp process group. default None means 1.
      dp_degree: Optional[int], data parallelism degree. How many processes are inside a dp process group. . default None means len(ranks).
docstr: cpu_tp_process_group
  
  the pytorch CPU TP process group containing the current rank.
  
  assert failed if cpu process group is not initialized.
  
  Returns:
      `torch._C._distributed_c10d.ProcessGroup`: the pytorch TP process group.
docstr_tok: ["cpu_tp_process_group","the","pytorch","CPU","TP","process","group","containing","the","current","rank",".","assert","failed","if","cpu","process","group","is","not","initialized",".","Returns",":","`","torch._C._distributed_c10d.ProcessGroup","`",":","the","pytorch","TP","process","group","."]
code: def cpu_tp_process_group(self):
      """cpu_tp_process_group
  
      the pytorch CPU TP process group containing the current rank.
  
      assert failed if cpu process group is not initialized.
  
      Returns:
          `torch._C._distributed_c10d.ProcessGroup`: the pytorch TP process group.
      """
      assert self._has_cpu_groups
      return PYTORCHPGDICT_.get(self._tp_rank_list, "gloo")
  
imports: from typing import List, Optional
  import torch
  from colossalai.context.singleton_meta import SingletonMeta
  from colossalai.logging import get_dist_logger
label_desc: Use image node_id 18 for calling the ProcessGroup obj's underlying member method code with example usage: obj.cpu_tp_process_group() and returns: PYTORCHPGDICT_